library(tidyverse)
## -- Attaching packages ------------------------------------------------------------------------------------ tidyverse 1.2.1 --
## v ggplot2 3.2.1 v purrr 0.3.2
## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 0.8.3 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0
## -- Conflicts --------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(ggplot2)
library(readr)
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
MetroPriceCut1 <- read_csv("MetroPriceCut1.csv")
## Parsed with column specification:
## cols(
## Date = col_character(),
## PriceCut = col_double(),
## RegionID = col_double(),
## RegionName = col_character(),
## SizeRank = col_double()
## )
StatePriceCut1 <- read_csv("StatePriceCut1.csv")
## Parsed with column specification:
## cols(
## Date = col_character(),
## PriceCut = col_double(),
## RegionID = col_double(),
## RegionName = col_character(),
## SizeRank = col_double()
## )
TopTierPC <- read_csv("PriceCutTopTier.csv")
## Parsed with column specification:
## cols(
## Date = col_character(),
## PriceCut = col_double(),
## RegionID = col_double(),
## RegionName = col_character(),
## SizeRank = col_double()
## )
MiddleTierPC <- read_csv("PriceCutMiddleTier.csv")
## Parsed with column specification:
## cols(
## Date = col_character(),
## PriceCut = col_double(),
## RegionID = col_double(),
## RegionName = col_character(),
## SizeRank = col_double()
## )
BottomTierPC <- read_csv("PriceCutBottomTier.csv")
## Parsed with column specification:
## cols(
## Date = col_character(),
## PriceCut = col_double(),
## RegionID = col_double(),
## RegionName = col_character(),
## SizeRank = col_double()
## )
StateMedianSqft <- read_csv("StateMedianValuePerSqft.csv")
## Parsed with column specification:
## cols(
## Date = col_character(),
## ValuePerSqft = col_double(),
## RegionID = col_double(),
## RegionName = col_character(),
## SizeRank = col_double()
## )
StateZHVI <- read_csv("StateZHVI.csv")
## Parsed with column specification:
## cols(
## Date = col_character(),
## ZHVI = col_double(),
## RegionID = col_double(),
## RegionName = col_character(),
## SizeRank = col_double()
## )
MetroZHVI <- read_csv("MetroZHVI.csv")
## Parsed with column specification:
## cols(
## Date = col_character(),
## ZHVI = col_double(),
## RegionNameCity = col_character(),
## RegionNameState = col_character(),
## RegionID = col_double(),
## RegionName = col_character(),
## SizeRank = col_double()
## )
g1 <- ggplot(StatePriceCut1[StatePriceCut1$RegionName == "Nevada" ,], aes(Date, PriceCut,
color = RegionName)) +
geom_point(alpha = 0.5) +
theme_minimal()
ggplotly(g1)
g2 <- ggplot(StatePriceCut1, aes(Date, PriceCut,
color = RegionName)) +
geom_point(alpha = 0.5) +
theme_minimal() +
facet_wrap(~RegionName) +
theme(legend.position = "none")
ggplotly(g2)
g3 <- ggplot(StateMedianSqft, aes(Date, ValuePerSqft,
color = RegionName)) +
geom_point(alpha = 0.5) +
theme_minimal() +
facet_wrap(~RegionName) +
theme(legend.position = "none")
ggplotly(g3)
`
g4 <- ggplot(StateZHVI, aes(Date, ZHVI,
color = RegionName)) +
geom_point(alpha = 0.5) +
theme_minimal() +
facet_wrap(~RegionName) +
theme(legend.position = "none")
ggplotly(g4)
g6 <- ggplot(MetroZHVI[MetroZHVI$RegionNameState == "CA" ,], aes(Date, ZHVI,
color = RegionName)) +
geom_point(alpha = 0.5) +
theme_minimal() +
theme(legend.position = "none") +
facet_wrap(~RegionNameCity)
ggplotly(g6)
g7 <- ggplot(MetroZHVI[MetroZHVI$RegionNameState == "TX" ,], aes(Date, ZHVI,
color = RegionName)) +
geom_point(alpha = 0.5) +
theme_minimal() +
theme(legend.position = "none") +
facet_wrap(~RegionNameCity)
ggplotly(g7)